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Found 13 papers, 10 papers with code
Machine Translation Meta Evaluation through Translation Accuracy Challenge Sets
We benchmark metric performance, assess their incremental performance over successive campaigns, and measure their sensitivity to a range of linguistic phenomena.
A Benchmark for Evaluating Machine Translation Metrics on Dialects Without Standard Orthography
For sensible progress in natural language processing, it is important that we are aware of the limitations of the evaluation metrics we use.
ACES: Translation Accuracy Challenge Sets at WMT 2023
We benchmark the performance of segmentlevel metrics submitted to WMT 2023 using the ACES Challenge Set (Amrhein et al., 2022).
Evaluating the Effectiveness of Natural Language Inference for Hate Speech Detection in Languages with Limited Labeled Data
In this paper, we test whether natural language inference (NLI) models which perform well in zero- and few-shot settings can benefit hate speech detection performance in scenarios where only a limited amount of labeled data is available in the target language.
Exploiting Biased Models to De-bias Text: A Gender-Fair Rewriting Model
We hypothesise that creating training data in the reverse direction, i. e. starting from gender-fair text, is easier for morphologically complex languages and show that it matches the performance of state-of-the-art rewriting models for English.
ACES: Translation Accuracy Challenge Sets for Evaluating Machine Translation Metrics
As machine translation (MT) metrics improve their correlation with human judgement every year, it is crucial to understand the limitations of such metrics at the segment level.
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Don't Discard Fixed-Window Audio Segmentation in Speech-to-Text Translation
For real-life applications, it is crucial that end-to-end spoken language translation models perform well on continuous audio, without relying on human-supplied segmentation.
Identifying Weaknesses in Machine Translation Metrics Through Minimum Bayes Risk Decoding: A Case Study for COMET
Neural metrics have achieved impressive correlation with human judgements in the evaluation of machine translation systems, but before we can safely optimise towards such metrics, we should be aware of (and ideally eliminate) biases toward bad translations that receive high scores.
How Suitable Are Subword Segmentation Strategies for Translating Non-Concatenative Morphology?
Data-driven subword segmentation has become the default strategy for open-vocabulary machine translation and other NLP tasks, but may not be sufficiently generic for optimal learning of non-concatenative morphology.
On Biasing Transformer Attention Towards Monotonicity
Many sequence-to-sequence tasks in natural language processing are roughly monotonic in the alignment between source and target sequence, and previous work has facilitated or enforced learning of monotonic attention behavior via specialized attention functions or pretraining.
On Romanization for Model Transfer Between Scripts in Neural Machine Translation
Our results show that romanization entails information loss and is thus not always superior to simpler vocabulary transfer methods, but can improve the transfer between related languages with different scripts.
Post-editing Productivity with Neural Machine Translation: An Empirical Assessment of Speed and Quality in the Banking and Finance Domain
Neural machine translation (NMT) has set new quality standards in automatic translation, yet its effect on post-editing productivity is still pending thorough investigation.
